معرفی
Suhas Diggavi is a Professor in the Department of Electrical and Computer Engineering at the University of California, Los Angeles, within the Henry Samueli School of Engineering and Applied Science. His primary research area is Signals and Systems, with a strong focus on information theory and its interdisciplinary applications.
His research interests span Information Theory, Machine Learning, Differential Privacy, Federated Learning, Cyber-Physical Systems, and Bio-informatics. He investigates fundamental limits and practical algorithms for secure, efficient, and robust data processing in distributed and networked environments.
The recent publications highlight a strong trend in privacy-preserving machine learning, particularly in the shuffled model of differential privacy, communication-efficient distributed SGD, and robust optimization. His work bridges theoretical information-theoretic foundations with real-world applications in federated learning, wireless networks, and genomic data analysis.
Notable scientific awards include:
- Guggenheim Foundation Fellow (2021)
- ACM CCS Best Paper Award (2021)
- IEEE Fellow (2013)
- IEEE Donald G. Fink Prize Paper Award (2006)
- Multiple Google, Amazon, and Facebook Research Awards
Suhas Diggavi actively advises graduate students and leads a research group focused on learning, information, and optimization. His work is supported by major industry grants and collaborations, particularly in privacy and distributed learning. He has made significant contributions to information-theoretic models in bio-sequencing and wireless security.
He leads the LIOS (Learning, Information, Optimization, and Stochastic Systems) research group at UCLA, where his team develops theoretical frameworks and practical algorithms for next-generation data-driven systems.
